Downscaling NPP–VIIRS Nighttime Light Data Using Vegetation Nighttime Condition Index
Bibliographic record
Abstract
Nighttime light (NTL) data, a cornerstone in the scientific community, are widely used across various disciplines. However, the spatial resolution of the commonly used NTL datasets often falls coarse for detailed urban-scale analyses. Current downscaling approaches for NTL data typically rely on extensive auxiliary datasets, limiting their applicability to large geographical regions. In response, we have developed a novel NTL downscaling method that directly uses the vegetation nighttime condition index (VNCI) as input to downscale the national polar-orbiting partnership–visible infrared imaging radiometer suite NTL product. To showcase the potential of this innovative approach, we downscaled the NTL data for mainland China from 2013 to 2021 using only normalized difference vegetation index (NDVI) data as input. Our results demonstrate that the downscaled NTL data not only preserve the accuracy of the original NTL data but also reveal more spatial details and is consistent with the Luojia 1-01 NTL data. Our experiments underscore the significant advantages of the proposed VNCI-based NTL downscaling approach, including its simplicity and minimal data entry requirements, as it only necessitates NDVI as input. This practical and straightforward approach holds great promise for NTL-based urban studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".